Forward Deployed AI Engineer
Build end-to-end products on a solid data foundation, with AI as a force multiplier.
About TechTorch
At TechTorch, we’re building the future of intelligent work. Our mission is to help companies design, build, and deploy AI agents that automate complex, real-world workflows — delivering reliability, measurable ROI, and massive efficiency gains. Here, you won’t just be playing with prompts or running endless proofs of concept. You’ll ship production-grade AI systems that solve real problems across industries. You’ll join a hands-on, fast-moving, ownership-driven team that thrives on building quickly, iterating fast, and seeing results in days — not months.
About the Practice
TechTorch's Data Practice sits at the intersection of enterprise data and applied AI. We design and build AI-native systems that don't just analyze the past — they actively drive decisions. Our work spans data infrastructure and pipelines, intelligent automation, and full-stack AI applications across industries. We work the way the best client-delivery teams now operate: small teams, deep ownership, no hand-offs at boundaries. We take problems from a client whiteboard to production, and we let AI do the heavy lifting wherever it earns its place.
The Role
We're looking for an engineer who builds across the full stack and owns the data underneath it. You can sit in a client session, shape the architecture, design the data foundation, and ship the application that runs on top of it — without handing off at the boundaries. The work spans client delivery and internal accelerator development. You map the problem, structure the solution, and own the outcome from end to end. AI coding agents are central to how we build — not a novelty, but the daily layer that lets a small team cover a lot of ground.
What You'll Do
- Own work end to end — from discovery and solution shaping through system design, build, and production deployment.
- Design and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production.
- Build full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usable.
- Use AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or quality.
- Design and build AI capabilities where they fit — RAG pipelines, agentic workflows, and LLM-in-the-loop processing — and compose them via MCP servers, Skills, and Plugins.
- Orchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the job.
- Stand up and own CI/CD and cloud deployments on AWS and Azure.
- Translate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiences.
- Contribute reusable accelerators and technical assets back to the Data Practice.
What We Offer
- Fully remote — work from anywhere, globally.
- Semi-annual team offsites — we come together in person at least twice a year to connect, recharge, and do the work that's better face-to-face.
- High-autonomy, high-ownership work across the full arc of real client problems — not toy datasets or boxed-in tickets.
- A team that takes AI tooling seriously and expects you to use it, not just name-drop it.
- Access to the full modern data and AI stack — no one-tool shops.
- Room to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.
Must Have
Data Engineering Foundation * Data modeling and schema design — dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defend. * Hands-on data pipeline experience — ETL/ELT design across batch and incremental loads, built and maintained in production (not just SQL scripts on a schedule). * Slowly Changing Dimensions (SCD) and change-data handling — knows the patterns and when each applies. * dbt Experience— modular SQL transformations, tests, documentation, and incremental strategies. * Advanced SQL and at least one modern data platform in depth (e.g., Snowflake, Databricks, or a comparable cloud warehouse/lakehouse). * Data quality thinking — testing, validation, and lineage treated as first-class, not afterthoughts.
Full-Stack AI Product Development * Python as a primary language — services, automation, and data work alike. * FastAPI — async REST API design, dependency injection, testing. * A modern frontend, ideally Next.js — component architecture, SSR, state management, and real UX sensibility. * PostgreSQL — schema design, query optimization, indexing. * System design — can architect from a blank page: services, boundaries, trade-offs, and scale. * AI-paired engineering — uses an agentic coding tool (Claude Code, Cursor, or comparable) as a genuine daily workflow accelerator, and can speak concretely to how. * CI/CD and cloud deployment ownership on AWS or Azure, without heavy support.
Ways of Working * Comfortable in client-facing delivery — can represent TechTorch technically and translate between business and engineering. * Customer-first mindset — anchors decisions in what the stakeholder is actually trying to accomplish, and can move fluidly between the engineer's view and the business owner's in the same conversation. * End-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each handoff.
Nice to Have
- Standout differentiator — Commercial data fluency: Experience evaluating how commercial data flows across CRM (ideally Salesforce) and ERP (ideally NetSuite) from opportunity to order to invoice, with the ability to diagnose, document, and resolve inconsistencies.
- Agentic AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state management.
- RAG engineering — retrieval strategies, vector stores, chunking, re-ranking, and evaluation.
- Experience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role.
- Workflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows).
- Streaming data patterns — Kafka, Spark Streaming, or Flink.
- Vector databases — Pinecone, Weaviate, Qdrant, or pgvector.
- Experiment tracking — MLflow, Weights & Biases, or similar.
- Contributions to open-source AI or data tooling, or to internal accelerators and frameworks.
- Multi-cloud or hybrid cloud architecture exposure.
